tulipy

Python bindings for Tulip Indicators

Tulipy requires numpy as all inputs and outputs are numpy arrays (dtype=np.float64).

Installation

You can install via pip install tulipy. If a wheel is not available for your system, you will need to pip install Cython numpy to build from the source distribution.

Usage



In [1]:

    
import numpy as np
import tulipy as ti



In [2]:

    
ti.TI_VERSION









    Out[2]:





'0.8.4'



In [3]:

    
DATA = np.array([81.59, 81.06, 82.87, 83,    83.61,
                 83.15, 82.84, 83.99, 84.55, 84.36,
                 85.53, 86.54, 86.89, 87.77, 87.29])

Information about indicators are exposed as properties:



In [4]:

    
def print_info(indicator):
    print("Type:", indicator.type)
    print("Full Name:", indicator.full_name)
    print("Inputs:", indicator.inputs)
    print("Options:", indicator.options)
    print("Outputs:", indicator.outputs)



In [5]:

    
print_info(ti.sqrt)









    



Type: simple
Full Name: Vector Square Root
Inputs: ['real']
Options: []
Outputs: ['sqrt']

Single outputs are returned directly. Indicators returning multiple outputs use a tuple in the order indicated by the outputs property.



In [6]:

    
ti.sqrt(DATA)









    Out[6]:





array([ 9.03271831,  9.00333272,  9.10329611,  9.11043358,  9.14385039,
        9.11866218,  9.1016482 ,  9.16460583,  9.19510739,  9.18477   ,
        9.24824308,  9.30268778,  9.32148057,  9.36856446,  9.34291175])



In [7]:

    
print_info(ti.sma)









    



Type: overlay
Full Name: Simple Moving Average
Inputs: ['real']
Options: ['period']
Outputs: ['sma']



In [8]:

    
ti.sma(DATA, period=5)









    Out[8]:





array([ 82.426,  82.738,  83.094,  83.318,  83.628,  83.778,  84.254,
        84.994,  85.574,  86.218,  86.804])

Invalid options will throw an InvalidOptionError:



In [9]:

    
try:
    ti.sma(DATA, period=-5)
except ti.InvalidOptionError:
    print("Invalid Option!")









    



Invalid Option!



In [10]:

    
print_info(ti.bbands)









    



Type: overlay
Full Name: Bollinger Bands
Inputs: ['real']
Options: ['period', 'stddev']
Outputs: ['bbands_lower', 'bbands_middle', 'bbands_upper']



In [11]:

    
ti.bbands(DATA, period=5, stddev=2)









    Out[11]:





(array([ 80.53004219,  80.98714192,  82.53334324,  82.47198345,
         82.41775044,  82.43520292,  82.51133078,  83.14261781,
         83.53648779,  83.8703237 ,  85.28887096]),
 array([ 82.426,  82.738,  83.094,  83.318,  83.628,  83.778,  84.254,
         84.994,  85.574,  86.218,  86.804]),
 array([ 84.32195781,  84.48885808,  83.65465676,  84.16401655,
         84.83824956,  85.12079708,  85.99666922,  86.84538219,
         87.61151221,  88.5656763 ,  88.31912904]))

If inputs of differing sizes are provided, they are right-aligned and trimmed from the left:



In [12]:

    
DATA2 = np.array([83.15, 82.84, 83.99, 84.55, 84.36])



In [13]:

    
# 'high' trimmed to DATA[-5:] == array([ 85.53,  86.54,  86.89,  87.77,  87.29])
ti.aroonosc(high=DATA, low=DATA2, period=2)









    Out[13]:





array([  50.,  100.,   50.])